






'''simple docstring'''







import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest




class   __SCREAMING_SNAKE_CASE       (__A       ):


  """simple docstring"""


  _a  : Tuple      			=     (DDPMParallelScheduler,)







  def    _a       (			self   ,	**UpperCamelCase__	):






         """simple docstring"""


         a_						     =			{
             'num_train_timesteps': 1_000,
             'beta_start': 0.0_001,
             'beta_end': 0.02,
             'beta_schedule': 'linear',
             'variance_type': 'fixed_small',
             'clip_sample': True,
         }

         config.update(**UpperCamelCase__	)
         return config







  def    _a       (			self	):






         """simple docstring"""


         for timesteps in [1, 5, 100, 1_000]:
                self.check_over_configs(num_train_timesteps=UpperCamelCase__	)







  def    _a       (			self	):






         """simple docstring"""


         for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]   ,	[0.002, 0.02, 0.2, 2]	):
                self.check_over_configs(beta_start=UpperCamelCase__   ,	beta_end=UpperCamelCase__	)







  def    _a       (			self	):






         """simple docstring"""


         for schedule in ["linear", "squaredcos_cap_v2"]:
                self.check_over_configs(beta_schedule=UpperCamelCase__	)







  def    _a       (			self	):






         """simple docstring"""


         for variance in ["fixed_small", "fixed_large", "other"]:
                self.check_over_configs(variance_type=UpperCamelCase__	)







  def    _a       (			self	):






         """simple docstring"""


         for clip_sample in [True, False]:
                self.check_over_configs(clip_sample=UpperCamelCase__	)







  def    _a       (			self	):






         """simple docstring"""


         self.check_over_configs(thresholding=UpperCamelCase__	)
         for threshold in [0.5, 1.0, 2.0]:
                for prediction_type in ["epsilon", "sample", "v_prediction"]:
                       self.check_over_configs(
                           thresholding=UpperCamelCase__   ,	prediction_type=UpperCamelCase__   ,	sample_max_value=UpperCamelCase__   ,	)







  def    _a       (			self	):






         """simple docstring"""


         for prediction_type in ["epsilon", "sample", "v_prediction"]:
                self.check_over_configs(prediction_type=UpperCamelCase__	)







  def    _a       (			self	):






         """simple docstring"""


         for t in [0, 500, 999]:
                self.check_over_forward(time_step=UpperCamelCase__	)







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.scheduler_classes[0]
         a_						     =			self.get_scheduler_config()
         a_						     =			scheduler_class(**UpperCamelCase__	)

         assert torch.sum(torch.abs(scheduler._get_variance(0	) - 0.0	)	) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(487	) - 0.00_979	)	) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(999	) - 0.02	)	) < 1e-5







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.scheduler_classes[0]
         a_						     =			self.get_scheduler_config()
         a_						     =			scheduler_class(**UpperCamelCase__	)

         a_						     =			len(UpperCamelCase__	)

         a_						     =			self.dummy_model()
         a_						     =			self.dummy_sample_deter
         a_						     =			self.dummy_sample_deter + 0.1
         a_						     =			self.dummy_sample_deter - 0.1

         a_						     =			samplea.shape[0]
         a_						     =			torch.stack([samplea, samplea, samplea]   ,	dim=0	)
         a_						     =			torch.arange(UpperCamelCase__	)[0:3, None].repeat(1   ,	UpperCamelCase__	)

         a_						     =			model(samples.flatten(0   ,	1	)   ,	timesteps.flatten(0   ,	1	)	)
         a_						     =			scheduler.batch_step_no_noise(UpperCamelCase__   ,	timesteps.flatten(0   ,	1	)   ,	samples.flatten(0   ,	1	)	)

         a_						     =			torch.sum(torch.abs(UpperCamelCase__	)	)
         a_						     =			torch.mean(torch.abs(UpperCamelCase__	)	)

         assert abs(result_sum.item() - 1_153.1_833	) < 1e-2
         assert abs(result_mean.item() - 0.5_005	) < 1e-3







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.scheduler_classes[0]
         a_						     =			self.get_scheduler_config()
         a_						     =			scheduler_class(**UpperCamelCase__	)

         a_						     =			len(UpperCamelCase__	)

         a_						     =			self.dummy_model()
         a_						     =			self.dummy_sample_deter
         a_						     =			torch.manual_seed(0	)

         for t in reversed(range(UpperCamelCase__	)	):
                # 1. predict noise residual
                a_						     =			model(UpperCamelCase__   ,	UpperCamelCase__	)

                # 2. predict previous mean of sample x_t-1
                a_						     =			scheduler.step(UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	generator=UpperCamelCase__	).prev_sample

                a_						     =			pred_prev_sample

         a_						     =			torch.sum(torch.abs(UpperCamelCase__	)	)
         a_						     =			torch.mean(torch.abs(UpperCamelCase__	)	)

         assert abs(result_sum.item() - 258.9_606	) < 1e-2
         assert abs(result_mean.item() - 0.3_372	) < 1e-3







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.scheduler_classes[0]
         a_						     =			self.get_scheduler_config(prediction_type='v_prediction'	)
         a_						     =			scheduler_class(**UpperCamelCase__	)

         a_						     =			len(UpperCamelCase__	)

         a_						     =			self.dummy_model()
         a_						     =			self.dummy_sample_deter
         a_						     =			torch.manual_seed(0	)

         for t in reversed(range(UpperCamelCase__	)	):
                # 1. predict noise residual
                a_						     =			model(UpperCamelCase__   ,	UpperCamelCase__	)

                # 2. predict previous mean of sample x_t-1
                a_						     =			scheduler.step(UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	generator=UpperCamelCase__	).prev_sample

                a_						     =			pred_prev_sample

         a_						     =			torch.sum(torch.abs(UpperCamelCase__	)	)
         a_						     =			torch.mean(torch.abs(UpperCamelCase__	)	)

         assert abs(result_sum.item() - 202.0_296	) < 1e-2
         assert abs(result_mean.item() - 0.2_631	) < 1e-3







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.scheduler_classes[0]
         a_						     =			self.get_scheduler_config()
         a_						     =			scheduler_class(**UpperCamelCase__	)

         a_						     =			[100, 87, 50, 1, 0]

         scheduler.set_timesteps(timesteps=UpperCamelCase__	)

         a_						     =			scheduler.timesteps

         for i, timestep in enumerate(UpperCamelCase__	):
                if i == len(UpperCamelCase__	) - 1:
                       a_						     =			-1
                else:
                       a_						     =			timesteps[i + 1]

                a_						     =			scheduler.previous_timestep(UpperCamelCase__	)
                a_						     =			prev_t.item()

                self.assertEqual(UpperCamelCase__   ,	UpperCamelCase__	)







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.scheduler_classes[0]
         a_						     =			self.get_scheduler_config()
         a_						     =			scheduler_class(**UpperCamelCase__	)

         a_						     =			[100, 87, 50, 51, 0]

         with self.assertRaises(UpperCamelCase__   ,	msg='`custom_timesteps` must be in descending order.'	):
                scheduler.set_timesteps(timesteps=UpperCamelCase__	)







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.scheduler_classes[0]
         a_						     =			self.get_scheduler_config()
         a_						     =			scheduler_class(**UpperCamelCase__	)

         a_						     =			[100, 87, 50, 1, 0]
         a_						     =			len(UpperCamelCase__	)

         with self.assertRaises(UpperCamelCase__   ,	msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.'	):
                scheduler.set_timesteps(num_inference_steps=UpperCamelCase__   ,	timesteps=UpperCamelCase__	)







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.scheduler_classes[0]
         a_						     =			self.get_scheduler_config()
         a_						     =			scheduler_class(**UpperCamelCase__	)

         a_						     =			[scheduler.config.num_train_timesteps]

         with self.assertRaises(
             UpperCamelCase__   ,	msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'   ,	):
                scheduler.set_timesteps(timesteps=UpperCamelCase__	)












'''simple docstring'''







def       __UpperCamelCase					(      lowercase_       :  list[int]   ,       lowercase_       :  list[int]   ,       lowercase_       :  int   ):
       """simple docstring"""




       return not any(
           neighbour == 1 and colored_vertices[i] == color
           for i, neighbour in enumerate(lowercase_   )   )


def       __UpperCamelCase					(      lowercase_       :  list[list[int]]   ,       lowercase_       :  int   ,       lowercase_       :  list[int]   ,       lowercase_       :  int   ):
       """simple docstring"""




       if index == len(lowercase_   ):
              return True

       # Recursive Step
       for i in range(lowercase_   ):
              if valid_coloring(graph[index]   ,       lowercase_   ,       lowercase_   ):
                     # Color current vertex
                     a_						     =			i
                     # Validate coloring
                     if util_color(lowercase_   ,       lowercase_   ,       lowercase_   ,       index + 1   ):
                            return True
                     # Backtrack
                     a_						     =			-1
       return False


def       __UpperCamelCase					(      lowercase_       :  list[list[int]]   ,       lowercase_       :  int   ):
       """simple docstring"""




       a_						     =			[-1] * len(lowercase_   )

       if util_color(lowercase_   ,       lowercase_   ,       lowercase_   ,       0   ):
              return colored_vertices

       return []




